Method and apparatus for reconstructing electrocardiogram (ecg) data

ABSTRACT

Systems and apparatus for synthesizing (generating) 12-lead ECG dataset from 3-lead ECG data. In particular, one or more transformation parameters may be determined that may be applied to 3-lead ECG dataset to generate 12-lead ECG data with particular speed and accuracy. The transformation parameters, which may include a plurality of matrices, may be determined from a synchronized patient&#39;s 12-lead ECG dataset and 3-lead ECG data. The 12-lead ECG dataset may be collected at a different time than the 3-lead ECG data. In some embodiments, the 12-lead ECG dataset and/or the 3-lead ECG dataset may be resampled prior to determining the transformation parameters.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/494,806, filed Oct. 5, 2021, titled “METHOD AND APPARATUS FORRECONSTRUCTING ELECTROCARDIOGRAM (ECG) DATA,” now U.S. Pat. No.11,445,963, which is herein incorporated by reference in its entirety.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specificationare herein incorporated by reference in their entirety to the sameextent as if each individual publication or patent application wasspecifically and individually indicated to be incorporated by reference.

FIELD

Described herein are methods and apparatuses for generatingelectrocardiogram display data. More particularly, described herein aremethods and apparatus (devices and systems) for generating 12-lead ECGdisplay dataset based on captured 3-lead ECG data.

BACKGROUND

Acute Myocardial Infarction (AMI, also referred to as heart attack)remains a leading cause of mortality in the developed world. Findingaccurate and cost-effective solutions for AMI diagnosis is vital.Survival of patients having AMI may depend critically on reducingtreatment delay, and particularly reducing the time between symptomonset and medical treatment. A technology that would enable AMIdiagnosis early after occurrence of AMI symptoms, for example, atpatient's home or wherever the patient may be, may significantlydecrease AMI mortality.

A 12-lead electrocardiogram (ECG) is a widely adopted tool used incardiac diagnostics. In general, before ECG dataset is captured,characteristic points on patient's body are identified and electrodesare positioned with respect to these points. During ECG dataset capture,electrical voltages between two or more electrodes are measured, andcorresponding ECG signals are called ECG leads. A conventional 12-leadECG uses 10 electrodes to generate the 12 ECG signals or leads.

Recent advancements in the treatment of AMI and other cardiac disordersmay include the use of a portable ECG device. In contrast to conventionECG equipment, the portable ECG devices may generate substantially fewleads. For example, some portable ECG device may provide 3 leads.

However, since the conventional 12-lead ECG has been widely adopted,clinician analysis of ECG dataset other than the 12-lead ECG isdifficult. Therefore, it may be advantageous to transform ECG datasetother than conventional 12-lead ECG dataset into a conventional 12-leadECG format.

SUMMARY OF THE DISCLOSURE

The systems, methods, and devices of this disclosure each have severalinnovate aspects, no single one of which is solely responsible for thedesirable attributes disclosed herein.

One innovative aspect of the subject matter described in this disclosurecan be implemented as a method for synthesizing electrocardiogram (ECG)data. The method may include receiving a first 12-lead ECG dataset for apatient associated with a first dataset collection time, receiving afirst 3-lead ECG dataset for the patient associated with a seconddataset collection time different than the first dataset collection time(e.g., separated by more than an hour, more than a day, more than aweek, etc.), determining a set of linear transformation parameters tosynthesize 12-lead ECG dataset based at least in part on the first12-lead ECG dataset and the first 3-lead ECG data, and synthesizing asecond 12-lead ECG dataset from a second 3-lead ECG dataset associatedwith a third collection time based at least in part on the set of lineartransformation parameters.

A method of generating electrocardiogram (ECG) data may include:receiving a current 3-lead ECG data recorded from a patient, wherein thecurrent 3-lead ECG data comprises three orthogonal or pseudo-orthogonalleads; generating a derived 12-lead ECG dataset from the current 3-leadECG data by applying a set of linear transformation parameters, whereinthe set of linear transformation parameters are determined based atleast in part on a prior 12-lead ECG dataset recorded from the patientat a first earlier collection time and a prior 3-lead ECG datasetrecorded from the patient at a second earlier collection time that isdifferent from the first earlier collection time, wherein the prior3-lead ECG dataset is synchronized with the prior 12-lead ECG dataset bydetermining a representative beat for both the prior 3-lead ECG datasetand the prior 12-lead ECG dataset, wherein the set of lineartransformation parameters comprises a set of transformation matricesthat synthesize the prior 12-lead ECG dataset from the prior 3-lead ECGdataset; and outputting the derived 12-lead ECG dataset.

The prior 3-lead ECG dataset may be synchronized with the prior 12-leadECG dataset by determining a representative beat that applies to boththe prior 3-lead ECG dataset and the prior 12-lead ECG dataset. Therepresentative beat may be representative of both the 12-lead ECGdataset and the 3-lead ECG dataset and may be the mean beat waveform(e.g., the component parts, such as the P, Q, R, S, and T segments(e.g., all or portion of the PR interval, all or portion of the QRScomplex, all or portion of the ST segment, etc.). The representativebeat may be identified as described herein, including by an arithmeticmean or average.

For example, a method of generating electrocardiogram (ECG) data mayinclude: receiving a current 3-lead ECG data recorded from a patient,wherein the current 3-lead ECG data comprises three orthogonal orpseudo-orthogonal leads; generating a derived 12-lead ECG dataset fromthe current 3-lead ECG data by applying a set of linear transformationparameters, wherein the set of linear transformation parameters aredetermined based at least in part on a prior 12-lead ECG datasetrecorded from the patient at a first earlier collection time and a prior3-lead ECG dataset recorded from the patient at a second earliercollection time that is different from the first earlier collectiontime, wherein the prior 3-lead ECG dataset is synchronized with theprior 12-lead ECG dataset by determining a median beat for both theprior 3-lead ECG dataset and the prior 12-lead ECG dataset, segmentingeach lead of the prior 12-lead ECG dataset and the prior 3-lead ECGdataset, wherein the set of linear transformation parameters comprises aset of transformation matrices that synthesize segments of the prior12-lead ECG dataset from segments of the prior 3-lead ECG dataset; andoutputting the derived 12-lead ECG dataset.

The prior 3-lead ECG dataset may be synchronized with the prior 12-leadECG dataset by further determining a cross-correlation between the prior3-lead ECG dataset and the prior 12-lead ECG dataset. The prior 3-leadECG dataset may be synchronized with the prior 12-lead ECG dataset byfurther aligning features of a QRS complex of the prior 12-lead ECGdataset with features of a QRS complex of the prior 3-lead ECG dataset.The prior 3-lead ECG dataset may be synchronized with the prior 12-leadECG dataset by further resampling at least one of the prior 12-lead ECGdataset and the prior 3-lead ECG dataset. The resampling may be in thefrequency domain.

The prior 3-lead ECG dataset may be synchronized with the prior 12-leadECG dataset by further determining the median beat for both the prior3-lead ECG dataset and the prior 12-lead ECG dataset based on aplurality of heartbeats. The prior 3-lead ECG dataset may besynchronized with the prior 12-lead ECG dataset by further determiningthe median beat for both the prior 3-lead ECG dataset and the prior12-lead ECG dataset by selecting a representative heartbeat from each ofthe prior 12-lead ECG dataset and the prior 3-lead ECG dataset.

Outputting the derived 12-lead ECG dataset may include displaying thederived 12-lead ECG dataset. As mentioned, the leads of the 3-lead ECGmay be orthogonal or pseudo-orthogonal.

A method of generating electrocardiogram (ECG) data may include:receiving a current 3-lead ECG data recorded from a patient, wherein thecurrent 3-lead ECG data comprises three orthogonal or pseudo-orthogonalleads; generating a derived 12-lead ECG dataset from the current 3-leadECG data by applying a set of linear transformation parameters, whereinthe set of linear transformation parameters are determined based atleast in part on a prior 12-lead ECG dataset recorded from the patientat a first earlier collection time and a prior 3-lead ECG datasetrecorded from the patient using orthogonal or pseudo-orthogonal leads ata second earlier collection time that is different from the firstearlier collection time, wherein the prior 3-lead ECG dataset issynchronized with the prior 12-lead ECG dataset by determining arepresentative beat for both the prior 3-lead ECG dataset and the prior12-lead ECG dataset, segmenting each lead of the prior 12-lead ECGdataset and the prior 3-lead ECG dataset, wherein the set of lineartransformation parameters comprises a set of transformation matricesthat synthesize segments of the prior 12-lead ECG dataset from segmentsof the prior 3-lead ECG dataset; and outputting the derived 12-lead ECGdataset.

Also described herein are methods for generating electrocardiogram (ECG)data that may include: accessing a first 12-lead ECG dataset for apatient associated with a first dataset collection time; accessing afirst 3-lead ECG dataset for the patient associated with a seconddataset collection time that is different from the first datasetcollection time; determining a set of linear transformation parametersbased at least in part on the first 12-lead ECG dataset and the first3-lead ECG dataset; receiving a second 3-lead ECG dataset from a thirdtime collection time; and outputting a second 12-lead ECG datasetsynthesized from the second 3-lead ECG dataset using the set of lineartransformation parameters.

In some variations, determining the set of linear transformationparameters may include synchronizing the first 3-lead ECG dataset withthe first 12-lead ECG data. In some embodiments, the synchronizing mayinclude determining a cross-correlation between the first 3-lead ECGdataset and the first 12-lead ECG data. In some other embodiments, thesynchronizing may include resampling at least one of the first 12-leadECG dataset and the first 3-lead ECG data. The resampling may be in thefrequency domain.

In some variations, the synchronizing may include determining medianbeats for the first 12-lead ECG dataset and the first 3-lead ECG data.In some cases, determining the median beats may include determining anaverage or median value of each of the first 12-lead ECG dataset and thefirst 3-lead ECG dataset based on a plurality of heartbeats. In someother cases, determining the median beats may include selecting arepresentative heartbeat from each of the first 12-lead ECG dataset andthe first 3-lead ECG data.

In some variations, determining the set of linear transformationparameters may include segmenting each lead of the first 12-lead ECGdataset and the first 3-lead ECG dataset and determining a set oftransformation matrices to synthesize segments of the first 12-lead ECGdataset from segments of the first 3-lead ECG data.

In some variations, the method may include displaying the second 12-leadECG data. In some other variations, determining the set of lineartransformation parameters may include pre-processing the first 12-leadECG dataset and the first 3-lead ECG data. In still other variations,the first 3-lead ECG may be orthogonal or pseudo-orthogonal. Theresulting orthogonal or pseudo-orthogonal dataset may include datasetsufficient to determine associated conventional 12-lead ECG data.

Also described herein are ECG systems configured to perform any of thesemethods. These systems may be configured to determine the transformationmatrix and/or form the 12-lead ECG from a currently recorded 3-lead(e.g. orthogonal or pseudo-orthogonal) ECG either locally (to thepatient), remotely, or a combination of locally and remotely. Forexample, an ECG system may include a compute node configured to receivea first 12-lead ECG dataset for the patient associated with a firstdataset collection time, receive a first 3-lead ECG dataset for thepatient associated with a second dataset collection time different thanthe first dataset collection time, and determine a set of lineartransformation parameters to synthesize 12-lead ECG dataset based atleast in part on the first 12-lead ECG dataset and the first 3-lead ECGdata. The ECG system may also include a portable ECG device configuredto provide a second 3-lead ECG dataset from the patient, where thecompute node is further configured to synthesize a second 12-lead ECGdataset from the second 3-lead ECG dataset associated with a thirdcollection time based at least in part on the set of lineartransformation parameters.

An electrocardiogram (ECG) system comprising: a portable ECG deviceconfigured to record a current 3-lead ECG dataset from a patient; and anon-transitory computer-readable storage medium comprising instructionsthat, when executed by one or more processors, cause the one or moreprocessors to perform operations comprising: access a first 12-lead ECGdataset for the patient associated with a first dataset collection time;access a first 3-lead ECG dataset for the patient associated with asecond dataset collection time different than the first datasetcollection time; and determine a set of linear transformation parametersto synthesize 12-lead ECG dataset based at least in part on the first12-lead ECG dataset and the first 3-lead ECG data, wherein the prior3-lead ECG dataset is synchronized with the prior 12-lead ECG dataset bydetermining a representative beat for both the first 3-lead ECG datasetand the first 12-lead ECG dataset, wherein the set of lineartransformation parameters comprises a set of transformation matricesthat synthesize the first 12-lead ECG dataset from the first 3-lead ECGdataset; receive the current 3-lead ECG dataset from the portable ECGdevice; synthesize a second 12-lead ECG dataset from the current 3-leadECG dataset based at least in part on the set of linear transformationparameters; and output the second 12-lead ECG dataset.

In some variations, the compute node may be further configured tosynchronize the first 3-lead ECG dataset to the first 12-lead ECG data.The synchronization may include a determination of a cross-correlationbetween the first 3-lead ECG dataset and the first 12-lead ECG data. Insome other variations, the synchronization may include an alignment offeatures of a QRS complex of the first 12-lead ECG dataset with featuresof a QRS complex of the first 3-lead ECG data. In still othervariations, the synchronization may include a resampling of at least oneof the first 12-lead ECG dataset and the first 3-lead ECG data. Theresampling may be in the frequency domain.

In some variations, the synchronization may include a determination ofmedian beats for the first 12-lead ECG dataset and the first 3-lead ECGdata. In some cases, the determination of median beats may include adetermination of an average or median value of each of the first 12-leadECG dataset and the first 3-lead ECG dataset based on a plurality ofheartbeats. In some other cases, the determination of median beats mayinclude a selection of a representative heartbeat from each of the first12-lead ECG dataset and the first 3-lead ECG data.

In some variations, the one or more processors (which may be referred toherein as a “compute node”) may be further configured to segment eachlead of the first 12-lead ECG dataset and the first 3-lead ECG datasetand determine a set of transformation matrices to synthesize segments ofthe first 12-lead ECG dataset from segments of the first 3-lead ECGdata.

In some variations, the compute node may be further configured togenerate display dataset based on the second 12-lead ECG data. In someother variations, the compute node may be further configured topre-process the first 12-lead ECG dataset and the first 3-lead ECG data.In some variations, the first 3-lead ECG dataset may be orthogonal orpseudo-orthogonal dataset with respect to the first 12-lead ECG data. Insome cases, the orthogonal or pseudo-orthogonal dataset may includedataset sufficient to determine associated conventional 12-lead ECGdata.

Another innovative aspect of the subject matter described in thisdisclosure can be implemented as a non-transitory computer-readablestorage medium comprising instructions that, when executed by one ormore processors (e.g., of a compute node), cause the compute node toperform operations comprising receiving a first 12-lead ECG dataset fora patient associated with a first dataset collection time, receiving afirst 3-lead ECG dataset for the patient associated with a seconddataset collection time different than the first dataset collectiontime, determining a set of linear transformation parameters tosynthesize 12-lead ECG dataset based at least in part on the first12-lead ECG dataset and the first 3-lead ECG dataset and synthesizing asecond 12-lead ECG dataset from a second 3-lead ECG dataset associatedwith a third collection time based at least in part on the set of lineartransformation parameters.

For example, a non-transitory computer-readable storage mediumcomprising instructions that, when executed by one or more processors ofone or more processors, cause the one or more processors to performoperations comprising: receiving a first 12-lead ECG dataset for apatient associated with a first dataset collection time; receiving afirst 3-lead ECG dataset for the patient associated with a seconddataset collection time different than the first dataset collectiontime; determine a set of linear transformation parameters based at leastin part on the first 12-lead ECG dataset and the first 3-lead ECG data,wherein the first 3-lead ECG dataset is synchronized with the first12-lead ECG dataset by determining a representative beat for both thefirst 3-lead ECG dataset and the first 12-lead ECG dataset, wherein theset of linear transformation parameters comprises a set oftransformation matrices that synthesize the first 12-lead ECG datasetfrom the first 3-lead ECG dataset; receive a second 3-lead ECG datasetcorresponding to the patient; synthesize a second 12-lead ECG datasetfrom the second 3-lead ECG dataset based at least in part on the set oflinear transformation parameters; and output the second 12-lead ECGdataset.

In some variations, execution of instructions for determining the set oflinear transformation parameters may cause the compute node to performoperations further comprising synchronizing the first 3-lead ECG datasetwith the first 12-lead ECG data. In some cases, the synchronizing mayinclude determining a cross-correlation between the first 3-lead ECGdataset and the first 12-lead ECG data. In some other cases, thesynchronizing may include aligning features of a QRS complex of thefirst 12-lead ECG dataset with features of a QRS complex of the first3-lead ECG data. In some embodiments, the synchronizing may includeresampling at least one of the first 12-lead ECG dataset and the first3-lead ECG data. In some cases, the resampling may be in the frequencydomain.

In some variations, the synchronizing may include determining medianbeats for the first 12-lead ECG dataset and the first 3-lead ECG data.

BRIEF DESCRIPTION OF THE DRAWINGS

Novel features of embodiments described herein are set forth withparticularity in the appended claims. A better understanding of thefeatures and advantages of the embodiments may be obtained by referenceto the following detailed description that sets forth illustrativeembodiments and the accompanying drawings.

FIG. 1 shows one variation of an ECG system, in accordance with someembodiments.

FIG. 2 is a simplified flow diagram illustrating the generation of12-lead ECG dataset from a 3-lead ECG data.

FIG. 3 . graphically shows a process flow for synthesizing 12-lead ECGdataset based on 3-lead ECG data.

FIG. 4 is a flowchart depicting an example method for determining lineartransformation matrices, in accordance with some embodiments.

FIG. 5 is a lead diagram that includes lead I from a 12-lead ECG and anX₁ lead from a 3-lead ECG, in accordance with some embodiments.

FIG. 6 is a diagram showing some fiducial points with respect to anexample ECG lead.

FIG. 7 is a flowchart depicting an example method for synthesizing12-lead ECG dataset from 3-lead ECG data, in accordance with someembodiments.

FIG. 8 is a graph of an example 12-lead ECG, in accordance with someembodiments.

FIG. 9 is a graph of an example 3-lead ECG data, in accordance with someembodiments.

FIG. 10 is a graph of an example synthesized 12-lead ECG data, inaccordance with some embodiments.

FIG. 11 shows a block diagram of a compute node, in accordance with someembodiments.

DETAILED DESCRIPTION

Electrocardiograms (ECGs) may graphically represent detected voltagesassociated with heart muscle contractions. The contractions, which aredue to depolarization and repolarization of the heart muscle, may bestudied by inspecting ECG graphs. A 12-lead ECG graph is a widelyadopted tool that provides 12 different electrical voltage captures ofdetected heart voltages. Clinician have been widely trained at inferringheart conditions through studying a patient's 12-lead ECG data.

Portable ECG devices have been developed that allow the collection ofECG data in settings other than a clinic or a doctor's office. Manyportable ECG devices, however, do not capture enough information togenerate conventional 12-lead ECG data. For example, some portable ECGdevices may only generate or capture 3-lead ECG data. Indeed, in somecases, patient records may only include 12-lead ECG data makinginterpretation of non 12-lead ECG data difficult.

Implementation of the subject matter described in this disclosure may beused to generate (synthesize) 12-lead ECG data from non-12-lead ECGdata. More particularly, the subject matter may describe the synthesisof conventional 12-lead ECG data from 3-lead ECG data, such asorthogonal or pseudo-orthogonal 3-lead ECG data. In some variations, thesynthesis/generation of the 12-lead ECG data may be based on applyingtransformation parameters to the 3-lead ECG data. The transformationparameters may be determined based on a patient's previously captured(recorded) 12-lead ECG data and the patient's 3-lead ECG data. Notably,the 12-lead ECG data may have been captured at a different time withrespect to the 3-lead ECG data. In some variations, the transformationparameters may include one or more matrices that may be applied to3-lead ECG data to synthesize related 12-lead ECG data. In this manner,a clinician may advantageously review 3-lead ECG data that may bepresented as conventional 12-lead ECG data. In some cases, the 3-leadECG data may be provided by a portable ECG device that may be used insettings other than a clinic, hospital, doctor's office or the like. Forexample, 3-lead ECG data may be captured by a patient at home,transmitted to a remote compute node and converted to conventional12-lead ECG data. This 12-lead ECG data may be transmitted to aclinician and displayed.

The transformation parameters may be determined by synchronizing andsegmenting the previously captured 12-lead ECG data and 3-lead ECG data.Next, least-squares analysis may be performed to deterministicallydetermine a set of transformation matrices that may be used tosynthesize 12-lead ECG data from 3-lead ECG data. Once determined, thetransformation parameters may be stored. In some cases, thetransformation parameters may be store remotely in a cloud-based storagesystem. 12-lead ECG data may be synthesized from 3-lead ECG data usingthese remotely stored transformation parameters.

FIG. 1 shows one variation of an ECG system 100, in accordance with someembodiments. The ECG system 100 may include a compact and portable ECGdevice 110 and a display device 140. The ECG device 110 may generate3-lead ECG data for a patient 120. In some cases, the patient 120 mayplace the ECG device 110 on his or her chest while touching one or morecontact electrodes with his or her hands. Furthermore, the ECG device110 may also have multiple electrodes on a surface in contact with thepatient 120. Thus, the ECG device 110 may contact the patient 120 withmultiple electrodes through the patient's fingers and/or chest. Throughthe electrodes, the ECG device may generate the 3-lead ECG data. Oneexample of an ECG device 110 is described in commonly owned U.S. Pat.No. 10,433,744, titled “MOBILE THREE-LEAD CARDIAC MONITORING DEVICE ANDMETHOD FOR AUTOMATED DIAGNOSTICS”, filed on Apr. 11, 2016, which claimspriority to U.S. provisional patent application No. 62/145,431, titled“MOBILE THREE-LEAD CARDIAC MONITORING DEVICE AND METHOD FOR AUTOMATEDDIAGNOSTICS” and filed on Apr. 9, 2015. These applications are hereinincorporated by reference in their entirety.

In some variations, the leads generated by the ECG device 110 may beorthogonal or pseudo-orthogonal. The 3-lead ECG data from the ECG device110 may include some or all of the information included in conventional12-lead ECG data. However, some clinicians may be unfamiliar withinterpreting cardiac data from the orthogonal or pseudo-orthogonal leadsfrom the ECG device 110.

The data from the ECG device 110 may be transmitted to a network 130.The network 130 may include remote (cloud-based) storage and/or one ormore remote compute nodes (not shown). In some variations, 12-lead ECGdata may be synthesized from the 3-lead ECG data provided by the ECGdevice 110 within the network 130. The 12 lead ECG data may be displayedon the display device 140. In this way, a clinician may advantageouslyreview the patient's ECG data in a conventional 12-lead format.Furthermore, the patient 120 may be located in a setting other than aclinic or doctor's office. Thus, the ECG device 110 may enable aclinician to remotely diagnose and treat a distant patient 120.

In some variations, the display device 140 may include one or moreprocessors capable of synthesizing 12-lead ECG data from 3-lead ECGdata. For example, the display device 140 may be a laptop computer, asmart display or monitor, a mobile phone, or any other feasible device.Thus, in some variations the network 130 may transmit the 3-lead ECGdata from the ECG device 110 to the display device 140. The displaydevice 140 may transform the 3-lead ECG data to 12-lead ECG data andthen display this data.

FIG. 2 is a simplified flow diagram 200 illustrating the generation of12-lead ECG data 210 from a 3-lead ECG data 220. In some variations, the3-lead ECG data 220 may be provided by the ECG device 110 of FIG. 1 .Although illustrated here as X₁, X₂, and X₃, the 3 leads from the ECGdevice 110 may have any feasible labels and, in some variations, mayinclude more than 3 leads. The leads from the ECG device 110 may beorthogonal or pseudo-orthogonal with respect to a lead-spaceencompassing a conventional 12-lead ECG such as the 12-lead ECG data210. Thus, most or all of the information that may be included in aconventional 12-lead ECG may be included (encoded) in the orthogonal orpseudo-orthogonal leads from the ECG device 110. In other words, theorthogonal or pseudo-orthogonal data includes data sufficient todetermine the conventional 12-lead ECG data. Although the 12 leads arelabeled I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6, the leadsof the 12-lead ECG data 210 may have any feasible label. In somevariations more or few leads may be generated from the 3-lead ECG data220.

In some embodiments, the 3-lead ECG 220 data may be processed with alinear transformation 240 that may generate or synthesize the 12-leadECG data 210. Thus, the 3-lead ECG data 220 from the ECG device 110 maybe transformed into 12-lead ECG data 210 that may be more easilyinterpreted by clinicians due to the widely adopted and understoodnature of conventional 12-lead ECG data. In some cases, the synthesized12-lead ECG data 210 may be displayed as conventional 12-lead ECG chartsfor analysis and review by clinicians.

In some variations, the linear transformation 240 may be based, at leastin part, on a patient's 12-lead ECG data (not shown) that may have beenpreviously captured (recorded) and stored. The relationship between thelinear transformation 240 and a patient's previous 12-lead ECG data isillustrated graphically in FIG. 3 .

FIG. 3 . graphically shows a process flow 300 for determining orsynthesizing 12-lead ECG data based on 3-lead ECG data. Althoughdescribed with respect to 3-lead ECG data, the process described hereinmay be adapted for use with any feasible ECG data that is different thanthe conventional 12-lead ECG data.

The process flow 300 may use a patient's 12-lead ECG data 310 that hasbeen previously recorded and/or captured and the patient's 3-lead ECGdata 320. The 12-lead ECG data 310 may be from any feasible ECG devicecapable of generating and/or recording 12-lead ECG data. In somevariations, the 12-lead ECG data 310 may be a baseline ECG recordingwith respect to a patient's known physical state. In some cases, the12-lead ECG data 310 may be from health records associated with thepatient. For simplicity, the 12-lead ECG data 310 is shown to include I,II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6 leads, but in otherembodiments, the 12-lead ECG data 310 may include any feasible leads.

The 3-lead ECG data 320 may include any feasible 3-lead orthogonal orpseudo-orthogonal ECG data. In some variations, the 3-lead ECG data 320may be provided by a compact/portable ECG device, such as the ECG device110 of FIG. 1 . For simplicity, the 3-lead ECG data 320 is shown toinclude X₁, X₂, and X₃ leads, but any feasible 3-lead ECG data may beincluded. Notably, the 12-lead ECG data 310 and the 3-lead ECG data 320may be collected (e.g., captured or recorded) at different times. Inother words, the 12-lead ECG data 310 may be collected/recorded at afirst time and the 3-lead ECG data 320 may be collected/recorded asecond time that is different from the first time.

The 12-lead ECG data 310 and the 3-lead ECG data 320 may undergo ananalysis 330 to determine a relationship between the two. In some cases,the analysis 330 may determine a linear relationship between the 3-leadECG data 320 and the 12-lead ECG data 310. In some variations, thelinear relationship may include one or more linear transformationmatrices 340 that may be used to transform 3-lead ECG data 320 to12-lead ECG data (not shown). In other words, the linear transformationmatrices 340 may be used to synthesize and/or display 12-lead ECG dataderived from 3-lead ECG data 320. In this manner, a patient's 3-lead ECGdata 320 may be presented as a conventional 12-lead ECG data enabling aclinician to diagnose the patient's cardiac health. In some cases, the3-lead ECG data 320 may be provided by the patient in a home setting orotherwise away or separated from traditional medical facilities. Forexample, the patient may collect the 3-lead ECG data 320 with the ECGdevice 110 at home. Thus, extended cardiac care may be provided topatients distant or separated from specialized cardiac care facilities.

FIG. 4 is a flowchart depicting an example method 400 for determininglinear transformation matrices, in accordance with some embodiments.Some examples may perform the operations described herein withadditional operations, fewer operations, operations in a differentorder, operations in parallel, and some operations differently. Themethod 400 may be performed in conjunction with one or more processorslocally (with respect to the patient) or remotely by a processor orcomputer, such as a cloud-based processing node. In other variations,the method 400 may be performed by any other suitable system or device.

The method 400 may sometimes be associated with a “calibration phase”during which the linear transformation matrices are “calibrated” totransform 3-lead ECG data to 12-lead ECG data. As an overview to theprocess, 12-lead ECG data that was recorded/captured at a first time isaligned to 3-lead ECG data that was recorded/captured at a second time.After alignment, the ECG data is segmented, and transformation matricesassociated with the ECG data segments are determined.

The method 400 begins in block 402 where a patient's 12-lead ECG dataassociated with a first collection time (e.g., a data collection time)is received. The 12-lead ECG data may be collected and/or recorded withany feasible ECG equipment. In some variations, the 12-lead ECG data maybe received from health care records, including electronic health carerecords. In some cases, the 12-lead ECG data may be stored in one ormore remote file systems, such as cloud-based systems. Thus, the 12-leadECG data may be accessed through one or more networks including, forexample, the internet. As noted, the 12-lead ECG data may be associatedwith a first collection time. That is, the 12-lead ECG data may becaptured and/or recorded with respect to a first time or time period.

Next, in block 404, the patient's 3-lead ECG data associated with asecond collection time is received. The 3-lead ECG data may be collectedand/or recorded by any feasible ECG equipment, such as the ECG device110 of FIG. 1 . The 3-lead ECG data may be orthogonal orpseudo-orthogonal ECG data and may be captured and/or recorded withrespect to a second time or time period. Notably, the first time or timeperiod may be different than the second time or time period. In somecases, the 3-lead ECG data may be stored on a remote file system andaccessed through one or more networks.

Next, in block 406, the 12-lead ECG data and the 3-lead ECG data may bepre-processed. This operation may be optional, as illustrated withdashed lines in FIG. 4 . Signal pre-processing may include noisefiltering, baseline wandering removal, or any other feasiblepre-processing operations. For example, pre-processing operations forthe 12-lead ECG data and/or the 3-lead ECG data may include low-passfiltering to bandwidth limit the respective data signals and removeout-of-band noise.

Next, in block 408, median beats are generated for the 3-lead ECG dataand the 12-lead ECG data. In some embodiments, a median beat may begenerated for each lead of the associated ECG data. To generate a medianbeat, different beats of a particular lead of ECG data are aligned. Insome cases, the alignment may be determined by a cross-correlation ofQRS complexes included in the heartbeat of the selected lead. After theECG data of the selected lead is aligned, then median values associatedwith the particular ECG lead may be determined to determine the medianbeat. Although described herein as a median operation, in some otherembodiments, any other feasible selection operation may be usedincluding, but not limited to, a mean operation, a mode operation, orthe like. In some variations, instead of determining a median beat foreach ECG data lead, a representative lead (e.g., a representativeheartbeat) may be selected.

Next, in block 410, the median beats for the 3-lead and 12-lead ECG dataare synchronized. Since the 12-lead ECG data may have been captured at adifferent time than the 3-lead ECG data, the time periods of the 12-leadECG data may be different than the time periods of the 3-lead ECG data.Thus, prior to determining any ECG data relationships, the 3-lead ECGdata should be synchronized and/or normalized with respect to the12-lead ECG data. In some variations, the ECG data may be synchronizedwith respect to intervals. For example, the ECG data may be synchronizedwith respect to depolarization intervals and with respect torepolarization intervals.

FIG. 5 is a lead diagram 500 that includes lead I 510 from a 12-lead ECGand an X₁ lead 520 from a 3-lead ECG, in accordance with someembodiments. A depolarization interval 530 may begin with atrialdepolarization (e.g., a beginning of a heartbeat when atria contract)and may end after the QRS complex (depicted as point J in the leaddiagram 500). In some embodiments, synchronization of the ECG data withrespect to the depolarization intervals may include cross-correlation ofa first ECG data lead with a second ECG data lead. For example, thecross-correlation may be used to align the I lead 510 with the X₁ lead520 by using distinct features of the QRS complex included in each ECGdata lead. In some variations, the synchronization may include adetermination of a time shift between the I lead 510 and the X₁ lead520. After the determination of the time shift, the other leads of the3-lead ECG (e.g., the X₂ and the X₃ leads, not shown here forsimplicity) may be shifted by the same or a similar amount.

A repolarization interval 535 may begin at the J point and extend to theend of the heartbeat. Synchronization during the repolarization interval535 may include resampling the shorter (in time) ECG lead to be closerin length to the longer ECG lead. The portion of the ECG leads after thepoint J may be divided into two sections. The first section begins atthe point J and extends to T_(max). (T_(max) may be a relative maximumafter the point J.) The second section begins at T_(max) and extends tothe end of the heartbeat, T_(end).

First, the length of the X₁ lead 520 is compared to the length of the Ilead 510 within the interval between point J to T_(max). The shorter ECGis resampled so that its length is extended to approximately match thatof the longer ECG. In the example of FIG. 5 , the length of the I lead510 from the point J to T_(max) is longer than the respective length ofthe X₁ lead 520. Thus, the X₁ lead 520 may be resampled to approximatelymatch the length of the I lead 510 between point J and T_(max).

In some variations, the resampling of the ECG leads may occur in thefrequency domain by adding zero samples to the shorter lead. Forexample, if the section of the I lead 510 between the point J andT_(max) has M samples and the equivalent section of the X₁ lead 520 hasN samples, then M-N zero-valued samples may be added to the center ofthe frequency spectrum of the X₁ lead 520 before transforming back tothe time domain. In some other variations, the resampling may occur inthe time domain.

The second section (between T_(max) and T_(end)) of the X₁ lead 520 andthe second section of I lead 510 may be resampled in the same of asimilar manner. The resampled ECG lead may be filtered by fitting to apolynomial, such as a 10^(th) order polynomial, to reduce noise. Aresulting resampled and filtered X₁ lead 540 is shown in FIG. 5 forreference. The same resampling operations may be performed with respectto the X₂ and the X₃ ECG leads.

Returning to the FIG. 4 , in block 412, the median beat of the 12-leadECG data and the synchronized 3-lead ECG data are divided into a numberof segments. In some variations, the 12-lead and 3-lead ECG data may bedivided into three segments, however other numbers of segments arepossible. The segments may be based on fiducial points that may beidentified on each ECG lead. In some variations, the fiducial points ofP_(start), Q, J, and T_(end) may be used.

FIG. 6 is a diagram 600 showing some fiducial points with respect to anexample ECG lead. P_(start) may refer to a beginning the ECG lead.Notably, the P_(start) fiducial occurs before the P-wave which is afirst positive ECG deflection within a heartbeat period illustrated inFIG. 6 . The Q fiducial is associated with the beginning of the QRScomplex portion of the ECG lead. The QRS complex includes the Q, R, andS waves and is associated with the contraction of the ventricles of theheart. The J fiducial is associated with the end of the QRS complex. TheT_(end) fiducial is the end of the ECG lead.

In some variations, the fiducial points may be automatically determined,for example by computer algorithms or programs executed by one or moreprocessors. Example methods are discussed by Sun, Y et. al. (2005).Characteristic wave detection in ECG signal using morphologicaltransform. BMC Cardiovasc Disord, September 20;5:28 and Rakshit, M et.al. (2015). EKF with PSO technique for delineation of P and T wave inelectrocardiogram (ECG) signal. 2^(nd) International Conference onSignal Processing and Integrated Networks (SPIN), IEEE.

Additionally, a PQ fiducial point may be determined. The PQ fiducialpoint is located in the interval between [Q−60 milliseconds (ms), Q−20ms]. Note that Q is associated with the beginning of the Q wave of theQRS complex described above. The PQ fiducial point is based on a minimumvalue of the 3-lead ECG data during the [Q−60 ms, Q−20 ms] interval. Theminimum value may be based on a vector magnitude of the orthogonal orpseudo-orthogonal leads and may be expressed as V_(m)=√{square root over(x₁ ²+x₂ ²+x₃ ²)}.

Based on the determined fiducial points, three segments may bedetermined for each ECG lead. Example segments may include a firstsegment (T_(P)) defined as [Pstart−20 ms, PQ+20 ms], a second segment(T_(QRS)) defined as [PQ−20 ms, J+20 ms], and a third segment (T_(T))defined as [J−20 ms, T_(end)]. Example segments T_(P), T_(QRS), andT_(T) are shown in FIG. 6 for reference. In some embodiments, the threesegments may overlap. Overlapping segments may enable later determinedtransformation functions and/or matrices to smooth discontinuitiesbetween segments

Next, in block 414, patient-specific transformation parameters aredetermined with respect to the determined segments. In some variations,the transformation parameters may be expressed as linear transformationmatrices (e.g., linear transformation parameters). Four transformationmatrices may be defined: One transformation matrix may be associatedwith each segment defined in block 412 (e.g., T_(P), T_(QRS), and T_(T)segments) and a fourth matrix associated with a transition regionbetween the QRS complex and the T wave.

In some variations, matrices associated with the T_(P), T_(QRS), andT_(T) segments may be denoted as T_(P), T_(QRS), T_(T). These matricesmay be defined as

T _(k) =X _(k) ^(†) *Y _(k)  (eq. 1)

where: ÷ is the Moore-Penrose pseudo-inverse operator (i.e., X_(k)^(†)=(X_(k) ^(T)*X_(k))⁻¹*X_(k) ^(T);

X=(X₁, X₂, X₃) are the leads from the 3-lead ECG synchronized to the12-lead ECG;

Y=(Y₁, Y₂ . . . Y₁₂) are the leads from the 12-lead ECG; and

k=P, QRS, or T segments.

In some embodiments, the Y vector may include 8 independent leads and 4other leads based on Y₁ and Y₂ limb leads. In some other embodiments,the transformation matrices may be calculated using a least squaresmethod. For example:

T _(k)=(X _(k) ^(T) *W*X _(k))⁻¹ *X _(k) ^(T) *W *Y _(k)(eq. 2)

where W is a diagonal matrix containing weights for each sample withinsegment k:

${W = \begin{bmatrix}W_{1,k} & 0 & 0 \\0 & \ldots & 0 \\0 & 0 & W_{n,k}\end{bmatrix}};$

n is a number of samples within segment k; and

W_(i,k), (for i=1 . . . n) is a weight for the i-th sample calculated as

W _(i,k)=√{square root over (x _(1,i,k) ² +x _(2,i,k) ² +x _(3,i,k) ²)}

In some variations, a number of heartbeats for each lead beat may beconcatenated and then the above equation (equation 2) applied. Forexample, five or more beats may be concatenated, however, any number ofbeats may be used, including 1.

In some other variations, equation 2 may be applied separately to eachheartbeat j, thereby forming a collection of matrices {T_(P,j)},{T_(QRS,j)}, and {T_(T,j)}. Then, a median value for each set ofrespective coefficients within each collection of matrices may becalculated.

A fourth matrix may be used to synthesize the region between the QRScomplex and the T-wave. This region is sometimes referred to as the “STsegment” referencing the segment between conventional S and T waves onan ECG trace. In some variations, a transient matrix T_(transient) maybe a weighted combination of T_(QRS) and T_(T) matrices as expressedbelow in equation 3:

$\begin{matrix}{T_{transient} = {T_{QRS} + {\frac{P_{j,T}}{P_{j,{QRS}} + P_{j,T}}\left( {T_{T} - T_{QRS}} \right)}}} & \left( {{eq}.3} \right)\end{matrix}$

where: P_(j,QRS) and P_(j,T) represent a power associated with the QRSand T segments with respect to the j-th heartbeat

P _(j,k)=√{square root over (Σ_(i=1) ^(N) ^(j,k) (x _(1,i) ² +x _(2,i) ²+x _(3,i) ²))}  (eq. 4)

k=T, QRS;

i=is a sample number (such that i=1,2, . . . Nj,k);

Nj,k is a total number of samples for the j-th heartbeat and segment k;and

x₁, x₂, and x₃ are values for the leads of the 3-lead ECG of the segmentk and the j-th heartbeat.

Thus, the linear transformations T_(P), T_(QRS), T_(T), andT_(transient) may generate (as outputs) 12-lead ECG data based on 3-leadECG data (as inputs). In particular, the linear transformations maygenerate/synthesize segments of the 12-lead ECG data based on segmentsof the 3-lead ECG data. In some embodiments, the segments of the 12-leadECG data may overlap. The overlap may help smooth discontinuities withinthe generated (synthesized) 12-lead ECG data.

The linear transformations T_(P), T_(QRS), T_(T), and T_(transient) maybe stored for later use. For example, the linear transformations may beused to synthesize 12-lead ECG data based on 3-lead ECG data that hasbeen recorded/captured at a later time with respect to the 3-lead ECGdata that has been used to determine the linear transformations. In someembodiments, the transformations may be stored in cloud storage, orwithin any feasible medium.

The synthesized 12-lead ECG data may include ECG display data. Thus, insome embodiments the 12-lead ECG display data may be displayed to aclinician thereby enabling the clinician to provide a cardiacexamination and diagnosis for a patient based on 3-lead ECG data. Inother words, the 3-lead ECG data may be advantageously transformed intoconventional appearing 12-lead ECG data to enable cardiac examination.

FIG. 7 is a flowchart depicting an example method 700 for synthesizing12-lead ECG data from 3-lead ECG data, in accordance with someembodiments. In some variations, the 3-lead ECG data may includeorthogonal or pseudo-orthogonal ECG lead data. The method 700 maysometimes be referred to as a “monitoring phase” during which thetransformation matrices (determined with respect to the method 400) areused to synthesize 12-lead ECG data that may be displayed and reviewedby a clinician.

The method 700 begins in block 702 where 3-lead ECG data is received. Insome variations, the 3-lead ECG data may be provided (received from) bythe ECG device 110 of FIG. 1 . In other variations, the 3-lead ECG datamay be provided by a remote server such as a cloud-based storage device.In some variations, the 3-lead ECG data may be orthogonal orpseudo-orthogonal ECG data (as described with respect to FIG. 2 ). Next,in block 704, fiducial points and fiducial related segments aredetermined with respect to the 3-lead ECG data. In some embodiments, thefiducial points may include P_(start), Q, J, and T_(end) points and maybe determined as described above with respect to FIG. 4 . Based on thesefiducial points, four synthesis segments may be defined. The foursegments may correspond to the T_(P), T_(QRS), T_(T) and T_(transient)segments described above with respect to FIG. 4 .

Next, in block 706, 12-lead ECG data is synthesized from the received3-lead ECG data for each of the above-defined segments. In someembodiments, the linear transformations described with respect to FIG. 4(e.g., the linear transformation matrices T_(P), T_(QRS), T_(T), andT_(transient)) may be used to synthesize each segment of the 12-lead ECGdata. For example, the T_(P) segment may be synthesized (e.g.,determined or reconstructed) by matrix multiplication of the received3-lead ECG data and the T_(P) matrix. The T_(QRS) segment may besynthesized by matrix multiplication of the received 3-lead ECG data bythe T_(QRS) matrix. The T_(T) segment may be synthesized by matrixmultiplication of the received 3-lead ECG data by the T_(T matrix). TheST segment may be synthesized by a weighted combination of theT_(transient), T_(QRS), and T_(T) matrices expressed below by equation5:

(x _(1,i) , x _(2,1) , x _(3,1))_(synth)=(x _(1,i) , x _(2,i) , x_(3,i))[J _(k)−20ms, J _(k)]*(w*T _(QRS)+ν*T_(transient))+(x _(1,i) , x_(2,i) , x _(3,i))[J _(k) , J _(k)+80ms]*(z*T _(transient) +q*T_(T))  (eq. 5)

where: i=is a sample number and ranges from 1,2, . . . N_(k);

N_(k) is the total number of samples for the k-th heartbeat;

x_(1,k), x_(2,k), and x_(3,k) are values for the 3-lead ECG during theST segment for the k-th heartbeat; and

w,v,z, and q are arbitrarily chosen weighting coefficients.

In some embodiments, the ST segment may be defined within the [J−20 ms,J+80 ms] interval. In some cases, with some weighting coefficients, thereconstruction matrix may be simplified to a single matrix, T_(single),the matrix T_(T), and the matrix T_(QRS). In another embodiment, the STsegment may be reconstructed with a population matrix, T_(POP).

Next, in block 708, discontinuities between the synthesized segments aresmoothed. In some cases, the smoothing is obtained by transitioningbetween matrices used in block 706 instead of abruptly changing betweenmatrices. One example is expressed below in equation 6:

$\begin{matrix}{T_{i} = {T_{i,k} + {\frac{i + {{tp}_{k + 1}/2}}{{tp}_{k + 1}}\left( {T_{i,{k + 1}} - T_{i,k}} \right)}}} & \left( {{eq}.6} \right)\end{matrix}$

where: Ti is a transient matrix applied to an i-th sample within atransient period;

k refers to T_(P), T_(QRS), T_(T), and T_(transient) segments;

i=tp_(k+13 1)/2, . . . , −1,0,1, . . . , tp_(k+1)/2 is the sample numbercalculated from the beginning of segment k+1; and

tpK+1 is a transition period.

In some variations, discontinuities that occur between segments of thesynthesized 12-lead ECG signals may be smoothed using one or morecubic-spline functions. Furthermore, although described as synthesizing12 leads of a 12-lead ECG, the method of FIG. 7 may be used tosynthesize fewer than 12-leads. For example, the method of FIG. 7 may beused to synthesize a subset of the 12 conventional ECG leads. In yetanother variation, the methods described herein may be used tosynthesize or reconstruct a missing lead of any well-defined set of ECGleads. In this case, the base or orthogonal/pseudo-orthogonal leads canbe any leads for which the missing ECG leads may be related.

FIG. 8 is a graph of an example 12-lead ECG data 800, in accordance withsome embodiments. The 12-lead ECG data 800 that may be captured(recorded) at a first collection time. As shown, the 12-lead ECG data800 may include the 12 conventional leads of I, II, III, aVR, aVL, aVF,V₁, V₂, V₃, V₄, V₅, and V₆. The 12-lead ECG data 800 may be used todetermine linear transformation parameters as described with respect toFIG. 4 .

FIG. 9 is a graph of an example 3-lead ECG data 900, in accordance withsome embodiments. In some variations, the 3-lead ECG data 900 may alsobe captured at the first collection time with respect to the 12-lead ECGdata 800. For example, the 3-lead ECG data 900 may represent ECG datacaptured by a portable ECG device 110 by a patient for analysis by aclinician. The 3-lead ECG data 900 may include three leads, such as X₁,X₂, and X₃, as described herein. The leads X₁, X₂, and X₃ may be labeleda, b, and c in FIG. 9 .

FIG. 10 is a graph of an example synthesized 12-lead ECG data 1000, inaccordance with some embodiments. For example, the synthesized 12-leadECG data 1000 may be based on the 3-lead ECG data 900 of FIG. 9 . Insome variations, a set of transformation parameters based on earlierrecorded 12-lead ECG data (not shown) may be used to synthesize the12-lead ECG data 1000. A visual comparison of the 12-lead ECG data 800and the synthesized 12-lead ECG data 1000 may show that the synthesized12-lead ECG data 1000 may be relatively similar to the 12-lead ECG data800. Table 1, below, shows mean differences and associated standarddeviations between the 12-lead ECG data 800 and the synthesized 12-leadECG data 1000. The relatively large mean numbers (e.g., numbersapproaching 1.0) indicate a large similarity between the 12-lead ECGdata 800 and the synthesized 12-lead ECG data 1000.

TABLE 1 Lead Mean Standard Deviation I 0.94253 0.062133 II 0.9403230.040819 III 0.840267 0.151103 aVR 0.949895 0.04622 aVL 0.888894 0.09409aVF 0.867555 0.239492 V1 0.924239 0.080143 V2 0.924775 0.087036 V30.901153 0.219118 V4 0.891144 0.252271 V5 0.951643 0.046939 V6 0.9536780.043724 Average 0.914675 0.051309

FIG. 11 shows a block diagram of a compute node 1100, in accordance withsome embodiments. The compute node 1100 may include a deviceinput/output (I/O) interface 1120, a processor 1130, and a memory 1140.The device I/O interface 1120, which may be coupled to a network (notshown), may transmit signals to and receive signals from other wired orwireless devices. For example, the device I/O interface 1120 maytransmit and receive data to/from a portable device, such as the ECGdevice 110 of FIG. 1 or any feasible display for the display of ECGdata. In some embodiments, the device I/O interface 1120 may transmitdata to handheld devices such as a smart phone, computing tables,laptops, or any other feasible device. Although not shown forsimplicity, a transceiver controller may be implemented within theprocessor 1130 and/or the memory 1140 to control transmit and receiveoperations of the device I/O interface 1120 including, for example,receiving 3-lead and 12-lead ECG data and transmitting synthesized12-lead ECG data and associated images.

The processor 1130, which is also coupled to the device I/O interface1120 and the memory 1140, may be any one or more suitable processorscapable of executing scripts or instructions of one or more softwareprograms stored within the compute node 1100 (such as within memory1140).

The memory 1140 may include a transformation parameter database 1142.The transformation parameter database 1142 may include one or moretransformation parameters for one or more patients. In some embodiments,the one or more transformation parameters may include lineartransformation matrices that may be used to synthesize 12-lead ECG datafrom 3-lead ECG data as described, for example, with respect to FIGS. 4and 7 . For example, the compute node 1100 may receive 3-lead ECG datathrough the device I/O interface 1120 and synthesize (generate) 12-leadECG data based on the 3-lead ECG data and one or more transformationparameters stored in the transformation parameter database 1142. Thesynthesized 12-lead ECG data may then the transmitted to any otherfeasible device through the device I/O interface 1120.

The memory 1140 may also include a non-transitory computer-readablestorage medium (e.g., one or more nonvolatile memory elements, such asEPROM, EEPROM, Flash memory, a hard-drive, etc.) that may store thefollowing software modules:

-   a transformation parameter determination software (SW) module 1144    to generate transformation parameters;-   a lead synthesis SW module 1146 to synthesize ECG leads; and a lead    display SW module 1147 to generate ECG lead data that may be    displayed.-   Each software module includes program instructions that, when    executed by the processor 1130, may cause the compute node 1100 to    perform the corresponding function(s). Thus, the non-transitory    computer-readable storage medium of the memory 1140 may include    instructions for performing all or a portion of the operations    listed herein.

The processor 1130 may execute the transformation parameterdetermination SW module 1144 to determine transformation parameters,including linear transformation matrices to generate (synthesize)12-lead ECG data from 3-lead ECG data. In some variations, thetransformation parameter determination SW module 1144 may includeinstructions to determine transformation matrices as described withrespect to FIG. 4 . In some embodiments, the determined transformationparameters may be stored in the memory 1140, such as in thetransformation parameter database 1142.

The processor 1130 may execute the lead synthesis SW module 1146 tosynthesize ECG leads based at least in part on transformationparameters. For example, execution of the lead synthesis SW module 1146may generate 12-lead ECG data based on received 3-lead ECG data andtransformation parameters. The transformation parameters may bedetermined with the transformation parameter determination SQ module1144 and/or stored in the transformation parameter database 1142. Insome variations, the lead synthesis SW module 1146 may includeinstructions to synthesize ECG data as described with respect to FIG. 7.

The processor 1130 may execute the lead display SW module 1147 togenerate ECG data that may be displayed to a clinician. For example,execution of the lead display SW module 1147 may generate 12-lead ECGdata that may be displayed, where the displayed data is based on the ECGdata synthesized based on the lead synthesis SW module 1146.

When a feature or element is herein referred to as being “on” anotherfeature or element, it can be directly on the other feature or elementor intervening features and/or elements may also be present. Incontrast, when a feature or element is referred to as being “directlyon” another feature or element, there are no intervening features orelements present. It will also be understood that, when a feature orelement is referred to as being “connected”, “attached” or “coupled” toanother feature or element, it can be directly connected, attached orcoupled to the other feature or element or intervening features orelements may be present. In contrast, when a feature or element isreferred to as being “directly connected”, “directly attached” or“directly coupled” to another feature or element, there are nointervening features or elements present. Although described or shownwith respect to one embodiment, the features and elements so describedor shown can apply to other embodiments. It will also be appreciated bythose of skill in the art that references to a structure or feature thatis disposed “adjacent” another feature may have portions that overlap orunderlie the adjacent feature.

Terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention.For example, as used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, steps, operations, elements, components, and/orgroups thereof. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items and may beabbreviated as “/”.

Spatially relative terms, such as “under”, “below”, “lower”, “over”,“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. For example, if a device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of over and under. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly”, “downwardly”, “vertical”, “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

Although the terms “first” and “second” may be used herein to describevarious features/elements (including steps), these features/elementsshould not be limited by these terms, unless the context indicatesotherwise. These terms may be used to distinguish one feature/elementfrom another feature/element. Thus, a first feature/element discussedbelow could be termed a second feature/element, and similarly, a secondfeature/element discussed below could be termed a first feature/elementwithout departing from the teachings of the present invention.

Throughout this specification and the claims which follow, unless thecontext requires otherwise, the word “comprise”, and variations such as“comprises” and “comprising” means various components can be co-jointlyemployed in the methods and articles (e.g., compositions and apparatusesincluding device and methods). For example, the term “comprising” willbe understood to imply the inclusion of any stated elements or steps butnot the exclusion of any other elements or steps.

In general, any of the apparatuses and methods described herein shouldbe understood to be inclusive, but all or a sub-set of the componentsand/or steps may alternatively be exclusive and may be expressed as“consisting of” or alternatively “consisting essentially of” the variouscomponents, steps, sub-components or sub-steps.

As used herein in the specification and claims, including as used in theexamples and unless otherwise expressly specified, all numbers may beread as if prefaced by the word “about” or “approximately,” even if theterm does not expressly appear. The phrase “about” or “approximately”may be used when describing magnitude and/or position to indicate thatthe value and/or position described is within a reasonable expectedrange of values and/or positions. For example, a numeric value may havea value that is +/−0.1% of the stated value (or range of values), +/−1%of the stated value (or range of values), +/−2% of the stated value (orrange of values), +/−5% of the stated value (or range of values), +/−10%of the stated value (or range of values), etc. Any numerical valuesgiven herein should also be understood to include about or approximatelythat value, unless the context indicates otherwise. For example, if thevalue “10” is disclosed, then “about 10” is also disclosed. Anynumerical range recited herein is intended to include all sub-rangessubsumed therein. It is also understood that when a value is disclosedthat “less than or equal to” the value, “greater than or equal to thevalue” and possible ranges between values are also disclosed, asappropriately understood by the skilled artisan. For example, if thevalue “X” is disclosed the “less than or equal to X” as well as “greaterthan or equal to X” (e.g., where X is a numerical value) is alsodisclosed. It is also understood that the throughout the application,data is provided in a number of different formats, and that this data,represents endpoints and starting points, and ranges for any combinationof the data points. For example, if a particular data point “10” and aparticular data point “15” are disclosed, it is understood that greaterthan, greater than or equal to, less than, less than or equal to, andequal to 10 and 15 are considered disclosed as well as between 10 and15. It is also understood that each unit between two particular unitsare also disclosed. For example, if 10 and 15 are disclosed, then 11,12, 13, and 14 are also disclosed.

Although various illustrative embodiments are described above, any of anumber of changes may be made to various embodiments without departingfrom the scope of the invention as described by the claims. For example,the order in which various described method steps are performed mayoften be changed in alternative embodiments, and in other alternativeembodiments one or more method steps may be skipped altogether. Optionalfeatures of various device and system embodiments may be included insome embodiments and not in others. Therefore, the foregoing descriptionis provided primarily for exemplary purposes and should not beinterpreted to limit the scope of the invention as it is set forth inthe claims.

The examples and illustrations included herein show, by way ofillustration and not of limitation, specific embodiments in which thesubject matter may be practiced. As mentioned, other embodiments may beutilized and derived there from, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. Such embodiments of the inventive subject matter maybe referred to herein individually or collectively by the term“invention” merely for convenience and without intending to voluntarilylimit the scope of this application to any single invention or inventiveconcept, if more than one is, in fact, disclosed. Thus, althoughspecific embodiments have been illustrated and described herein, anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. A method of synthesizing 12-leadelectrocardiogram (ECG) data, the method comprising: receiving a first12-lead ECG dataset for a patient associated with a first datasetcollection time; receiving a first 3-lead ECG dataset for the patientassociated with a second dataset collection time different than thefirst dataset collection time; determining a set of lineartransformation parameters to synthesize a second 12-lead ECG datasetbased at least in part on the first 12-lead ECG dataset and the first3-lead ECG data; and synthesizing the second 12-lead ECG dataset from asecond 3-lead ECG dataset associated with a third collection time basedat least in part on the set of linear transformation parameters.
 2. Themethod of claim 1, wherein the second dataset collection time isseparated from first dataset collection time by more than an hour. 3.The method of claim 1, wherein the second dataset collection time isseparated from first dataset collection time by more than a day.
 4. Themethod of claim 1, wherein the first 3-lead ECG dataset comprises threeorthogonal or pseudo-orthogonal leads.
 5. The method of claim 1, whereinthe first 3-lead ECG dataset is synchronized with the first 12-lead ECGdataset by determining a representative beat for both the first 3-leadECG dataset and the first 12-lead ECG dataset.
 6. The method of claim 1,wherein the first 3-lead ECG dataset is synchronized with the second12-lead ECG dataset by further resampling at least one of the first12-lead ECG dataset and the first 3-lead ECG dataset.
 7. The method ofclaim 6, wherein the resampling is in a frequency domain.
 8. The methodof claim 1, wherein the first 3-lead ECG dataset is synchronized withthe first 12-lead ECG dataset by determining a cross-correlation betweenthe first 3-lead ECG dataset and the first 12-lead ECG dataset.
 9. Themethod of claim 1, wherein the first 3-lead ECG dataset is synchronizedwith the first 12-lead ECG dataset by further aligning features of a QRScomplex of the first 12-lead ECG dataset with features of a QRS complexof the first 3-lead ECG dataset.
 10. The method of claim 1, wherein thefirst 3-lead ECG dataset is synchronized with the first 12-lead ECGdataset by further determining a representative beat for both the first3-lead ECG dataset and the first 12-lead ECG dataset based on a medianbeat or an average beat from a plurality of heartbeats.
 11. The methodof claim 1, wherein the first 3-lead ECG dataset is synchronized withthe first 12-lead ECG dataset by further determining a representativebeat for both the first 3-lead ECG dataset and the first 12-lead ECGdataset by selecting a median heartbeat from each of the first 12-leadECG dataset and the first 3-lead ECG dataset.
 12. The method of claim 1,wherein the set of linear transformation parameters comprises a set oftransformation matrices that synthesize the first 12-lead ECG datasetfrom the first 3-lead ECG dataset.
 13. The method of claim 1, furthercomprising outputting the second 12-lead ECG dataset.
 14. The method ofclaim 13, wherein outputting the second 12-lead ECG dataset comprisesdisplaying the second 12-lead ECG dataset.
 15. An electrocardiogram(ECG) system comprising: a portable ECG device configured to record acurrent 3-lead ECG dataset from a patient; and a non-transitorycomputer-readable storage medium comprising instructions that, whenexecuted by one or more processors, cause the one or more processors toperform operations comprising: receiving a first 12-lead ECG dataset fora patient associated with a first dataset collection time; receiving afirst 3-lead ECG dataset for the patient associated with a seconddataset collection time different than the first dataset collectiontime; determining a set of linear transformation parameters tosynthesize a second 12-lead ECG dataset based at least in part on thefirst 12-lead ECG dataset and the first 3-lead ECG data; synthesizingthe second 12-lead ECG dataset from a second 3-lead ECG datasetassociated with a third collection time based at least in part on theset of linear transformation parameters; and outputting the second12-lead ECG dataset.
 16. The system of claim 15, wherein the seconddataset collection time is separated from first dataset collection timeby more than an hour.
 17. The system of claim 15, wherein the seconddataset collection time is separated from first dataset collection timeby more than a day.
 18. The system of claim 15, wherein the first 3-leadECG dataset comprises three orthogonal or pseudo-orthogonal leads. 19.The system of claim 15, wherein the first 3-lead ECG dataset issynchronized with the first 12-lead ECG dataset by determining arepresentative beat for both the first 3-lead ECG dataset and the first12-lead ECG dataset.
 20. The system of claim 15, wherein the first3-lead ECG dataset is synchronized with the second 12-lead ECG datasetby further resampling at least one of the first 12-lead ECG dataset andthe first 3-lead ECG dataset.
 21. The system of claim 20, wherein theresampling is in a frequency domain.
 22. The system of claim 15, whereinthe first 3-lead ECG dataset is synchronized with the first 12-lead ECGdataset by determining a cross-correlation between the first 3-lead ECGdataset and the first 12-lead ECG dataset.
 23. The system of claim 15,wherein the first 3-lead ECG dataset is synchronized with the first12-lead ECG dataset by further aligning features of a QRS complex of thefirst 12-lead ECG dataset with features of a QRS complex of the first3-lead ECG dataset.
 24. The system of claim 15, wherein the first 3-leadECG dataset is synchronized with the first 12-lead ECG dataset byfurther determining a representative beat for both the first 3-lead ECGdataset and the first 12-lead ECG dataset based on a median beat or anaverage beat from a plurality of heartbeats.
 25. The system of claim 15,wherein the first 3-lead ECG dataset is synchronized with the first12-lead ECG dataset by further determining a representative beat forboth the first 3-lead ECG dataset and the first 12-lead ECG dataset byselecting a median heartbeat from each of the first 12-lead ECG datasetand the first 3-lead ECG dataset.
 26. The system of claim 15, whereinthe set of linear transformation parameters comprises a set oftransformation matrices that synthesize the first 12-lead ECG datasetfrom the first 3-lead ECG dataset.
 27. The system of claim 15, whereinthe instructions are further configured to output the second 12-lead ECGdataset.
 28. The system of claim 27, wherein outputting the second12-lead ECG dataset comprises displaying the second 12-lead ECG dataset.